|
--- |
|
library_name: peft |
|
license: cc-by-nc-4.0 |
|
language: |
|
- ja |
|
tags: |
|
- llama2 |
|
--- |
|
|
|
⚠️⚠️⚠️ |
|
Only for research purpose. |
|
Do not use it for medical purpose. |
|
⚠️⚠️⚠️ |
|
|
|
|
|
This model is an instruction-tuned model of Llama2-70B with our own medical Q&A dataset. |
|
|
|
## Method |
|
QLoRA |
|
|
|
## Parameters |
|
- batch_size = 512 |
|
- max_steps = 30000 (around 6.89 epochs) |
|
- source_max_len = 512 |
|
- target_max_len = 512 |
|
|
|
## Training time |
|
1617017 seconds on NVIDIA A100 x 4 (not fully used) |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
The following `bitsandbytes` quantization config was used during training: |
|
- quant_method: bitsandbytes |
|
- load_in_8bit: False |
|
- load_in_4bit: True |
|
- llm_int8_threshold: 6.0 |
|
- llm_int8_skip_modules: None |
|
- llm_int8_enable_fp32_cpu_offload: False |
|
- llm_int8_has_fp16_weight: False |
|
- bnb_4bit_quant_type: nf4 |
|
- bnb_4bit_use_double_quant: True |
|
- bnb_4bit_compute_dtype: bfloat16 |
|
### Framework versions |
|
- PEFT 0.4.0 |
|
|
|
### How to cite |
|
|
|
本データを利用する場合は以下の文献の引用をご検討ください. |
|
``` |
|
@article{sukeda2023jmedlora, |
|
title={{JMedLoRA: Medical Domain Adaptation on Japanese Large Language Models using Instruction-tuning}}, |
|
author={Sukeda, Issey and Suzuki, Masahiro and Sakaji, Hiroki and Kodera, Satoshi}, |
|
journal={arXiv preprint arXiv:2310.10083}, |
|
year={2023} |
|
} |
|
``` |
